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The objective of this article is to investigate whether it is possible to use Landsat data together with ancillary data and temporal context to accurately identify land covers found in the fallow areas of Montane Mainland Southeast Asia's (MMSEA's) difficult-to-map swidden landscapes. A rule-based non-parametric hybrid classification method that integrates knowledge about the vegetation regrowth patterns in these landscapes with analysis of Landsat imagery is developed. The method is applied to three upland districts of the Nghe An Province, Vietnam. The results show that the hybrid classification approach, with an overall accuracy of 90%, is superior to using a traditional maximum likelihood classifier, which generated an overall accuracy of 68%. The hybrid classification results indicate that the landscape is dominated by bush and bamboo, while the maximum likelihood classification suggests a landscape that is predominantly grass covered. The hybrid classification results are in agreement with local knowledge and information from fieldwork-based reports and articles on swidden systems in the study area and other parts of MMSEA.
